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      Prefrontal networks dynamically related to recovery from major depressive disorder: a longitudinal pharmacological fMRI study

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          Abstract

          Due to lacking predictors of depression recovery, successful treatment of major depressive disorder (MDD) is frequently only achieved after therapeutic optimization leading to a prolonged suffering of patients. This study aimed to determine neural prognostic predictors identifying non-remitters prior or early after treatment initiation. Moreover, it intended to detect time-sensitive neural mediators indicating depression recovery. This longitudinal, interventional, single-arm, open-label, phase IV, pharmacological functional magnetic resonance imaging (fMRI) study comprised four scans at important stages prior (day 0) and after escitalopram treatment initiation (day 1, 28, and 56). Totally, 22 treatment-free MDD patients (age mean ± SD: 31.5 ± 7.7; females: 50%) suffering from a concurrent major depressive episode without any comorbid DSM-IV axis I diagnosis completed the study protocol. Primary outcome were neural prognostic predictors of depression recovery. Enhanced de-activation of anterior medial prefrontal cortex (amPFC, single neural mediator) indicated depression recovery correlating with MADRS score and working memory improvements. Strong dorsolateral PFC (dlPFC) activation and weak dlPFC-amPFC, dlPFC-posterior cingulate cortex (PCC), dlPFC-parietal lobe (PL) coupling (three prognostic predictors) hinted at depression recovery at day 0 and 1. Preresponse prediction of continuous (dlPFC-PL: R 2 day1 = 55.9%, 95% CI: 22.6–79%, P < 0.005) and dichotomous (specificity/sensitivity: SP/SN day1 = 0.91/0.82) recovery definitions remained significant after leave-one-out cross-validation. Identified prefrontal neural predictors might propel the future development of fMRI markers for clinical decision making, which could lead to increased response rates and adherence during acute phase treatment periods. Moreover, this study underscores the importance of the amPFC in depression recovery.

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          Responses to depression and their effects on the duration of depressive episodes.

          Susan Nolen-Hoeksema (1991)
          I propose that the ways people respond to their own symptoms of depression influence the duration of these symptoms. People who engage in ruminative responses to depression, focusing on their symptoms and the possible causes and consequences of their symptoms, will show longer depressions than people who take action to distract themselves from their symptoms. Ruminative responses prolong depression because they allow the depressed mood to negatively bias thinking and interfere with instrumental behavior and problem-solving. Laboratory and field studies directly testing this theory have supported its predictions. I discuss how response styles can explain the greater likelihood of depression in women than men. Then I intergrate this response styles theory with studies of coping with discrete events. The response styles theory is compared to other theories of the duration of depression. Finally, I suggest what may help a depressed person to stop engaging in ruminative responses and how response styles for depression may develop.
          Article 0 comments Cited 793 times – based on 0 reviews     Review now
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            The neural bases of emotion regulation.

            Amit Etkin, Christian Büchel, James J Gross (2015)
            Emotions are powerful determinants of behaviour, thought and experience, and they may be regulated in various ways. Neuroimaging studies have implicated several brain regions in emotion regulation, including the ventral anterior cingulate and ventromedial prefrontal cortices, as well as the lateral prefrontal and parietal cortices. Drawing on computational approaches to value-based decision-making and reinforcement learning, we propose a unifying conceptual framework for understanding the neural bases of diverse forms of emotion regulation.
            Article 0 comments Cited 372 times – based on 0 reviews     Review now
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              Severity classification on the Hamilton Depression Rating Scale.

              Mark Zimmerman, Jennifer H Martinez, Diane Young … (2013)
              Symptom severity as a moderator of treatment response has been the subject of debate over the past 20 years. Each of the meta- and mega-analyses examining the treatment significance of depression severity used the Hamilton Depression Rating Scale (HAMD), wholly, or in part, to define severity, though the cutoff used to define severe depression varied. There is limited empirical research establishing cutoff scores for bands of severity on the HAMD. The goal of the study is to empirically establish cutoff scores on the HAMD in their allocation of patients to severity groups. Six hundred twenty-seven outpatients with current major depressive disorder were evaluated with a semi-structured diagnostic interview. Scores on the 17-item HAMD were derived from ratings according to the conversion method described by Endicott et al. (1981). The patients were also rated on the Clinical Global Index of Severity (CGI). Receiver operating curves were computed to identify the cutoff that optimally discriminated between patients with mild vs. moderate and moderate vs. severe depression. HAMD scores were significantly lower in patients with mild depression than patients with moderate depression, and patients with moderate depression scored significantly lower than patients with severe depression. The cutoff score on the HAMD that maximized the sum of sensitivity and specificity was 17 for the comparison of mild vs. moderate depression and 24 for the comparison of moderate vs. severe depression. The present study was conducted in a single outpatient practice in which the majority of patients were white, female, and had health insurance. Although the study was limited to a single site, a strength of the recruitment procedure was that the sample was not selected for participation in a treatment study, and exclusion and inclusion criteria did not reduce the representativeness of the patient groups. The analyses were based on HAMD scores extracted from ratings on the SADS. However, we used Endicott et al.'s (1981) empirically established formula for deriving a HAMD score from SADS ratings, and our results concurred with other small studies of the mean and median HAMD scores in severity groups. Based on this large study of psychiatric outpatients with major depressive disorder we recommend the following severity ranges for the HAMD: no depression (0-7); mild depression (8-16); moderate depression (17-23); and severe depression (≥24). Copyright © 2013 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                Lukas Pezawas:
                ORCID: http://orcid.org/0000-0002-1329-6352
                +43(1) 40400-30980 , lukas.pezawas@meduniwien.ac.at
                Journal
                Journal ID (nlm-ta): Transl Psychiatry
                Journal ID (iso-abbrev): Transl Psychiatry
                Title: Translational Psychiatry
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2158-3188
                Publication date (Electronic): 4 February 2019
                Publication date PMC-release: 4 February 2019
                Publication date Collection: 2019
                Volume: 9
                Electronic Location Identifier: 64
                Affiliations
                [1 ]ISNI 0000 0000 9259 8492, GRID grid.22937.3d, Division of General Psychiatry, Department of Psychiatry and Psychotherapy, , Medical University of Vienna, ; Vienna, Austria
                [2 ]ISNI 0000 0000 9259 8492, GRID grid.22937.3d, Department of Child and Adolescent Psychiatry, , Medical University of Vienna, ; Vienna, Austria
                [3 ]ISNI 0000 0000 9259 8492, GRID grid.22937.3d, MR Centre of Excellence, , Medical University of Vienna, ; Vienna, Austria
                [4 ]ISNI 0000 0000 9259 8492, GRID grid.22937.3d, Center for Medical Physics and Biomedical Engineering, , Medical University of Vienna, ; Vienna, Austria
                [5 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Psychiatry and Behavioral Sciences, , Stanford University School of Medicine, ; Palo Alto, CA USA
                [6 ]ISNI 0000 0004 0464 0574, GRID grid.416868.5, Scientific and Statistical Computational Core, , National Institute of Mental Health, ; Bethesda, MA USA
                Author information
                http://orcid.org/0000-0001-8278-9583
                http://orcid.org/0000-0002-1329-6352
                Article
                Publisher ID: 395
                DOI: 10.1038/s41398-019-0395-8
                PMC ID: 6362173
                PubMed ID: 30718459
                SO-VID: ba5caa75-6314-47e0-8b58-24458cb4a4ce
                Copyright © © The Author(s) 2019
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 12 June 2018
                Date revision received : 3 January 2019
                Date accepted : 10 January 2019
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2019

                ScienceOpen disciplines: Clinical Psychology & Psychiatry
                Data availability:
                ScienceOpen disciplines: Clinical Psychology & Psychiatry

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